We use a ResNet-50 model [1]}, pretrained on the 1.2M images from ImageNet [2]}, to extract 2048-dimensional images features and Doc2vec [3]}, pretrained on Wikipedia via skip-gram, to obtain 300-dimensional text features. All methods are fed with these pre-trained features except for VSE, which has raw text as input. We use hyperparameter settings following Xie et al. [4]} for DEC components. \(\lambda \) , \(\gamma \) , and \(\beta \)  are set to 0.5, 0.1, and 0.1 in all experiments, respectively. We will show that the model remains stable to different hyperparameter settings within reasonable range in next section. For baseline algorithms, we use the same setting in their corresponding paper. All the results are the average of 5 trials .
